{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x24cfe338760>"
      ],
      "text/html": "<style type=\"text/css\">\n#T_be470_row0_col0, #T_be470_row18_col2, #T_be470_row20_col0, #T_be470_row27_col2 {\n  background-color: #fee7dc;\n  color: #000000;\n}\n#T_be470_row0_col1, #T_be470_row0_col4, #T_be470_row1_col1, #T_be470_row1_col4, #T_be470_row2_col1, #T_be470_row2_col4, #T_be470_row3_col1, #T_be470_row3_col4, #T_be470_row4_col1, #T_be470_row4_col4, #T_be470_row5_col1, #T_be470_row5_col4, #T_be470_row6_col1, #T_be470_row6_col4, #T_be470_row7_col1, #T_be470_row7_col4, #T_be470_row8_col1, #T_be470_row8_col4, #T_be470_row9_col1, #T_be470_row9_col4, #T_be470_row10_col0, #T_be470_row10_col1, #T_be470_row10_col2, #T_be470_row10_col4, #T_be470_row11_col1, #T_be470_row11_col4, #T_be470_row12_col1, #T_be470_row12_col4, #T_be470_row13_col1, #T_be470_row13_col4, #T_be470_row14_col1, #T_be470_row14_col2, #T_be470_row14_col3, #T_be470_row14_col4, #T_be470_row15_col1, #T_be470_row15_col4, #T_be470_row16_col1, #T_be470_row16_col4, #T_be470_row17_col1, #T_be470_row17_col4, #T_be470_row18_col1, #T_be470_row18_col4, #T_be470_row19_col1, #T_be470_row19_col4, #T_be470_row20_col1, #T_be470_row20_col4, #T_be470_row21_col1, #T_be470_row21_col4, #T_be470_row22_col1, #T_be470_row22_col4, #T_be470_row22_col5, #T_be470_row23_col1, #T_be470_row23_col4, #T_be470_row23_col5, #T_be470_row24_col1, #T_be470_row24_col4, #T_be470_row24_col5, #T_be470_row25_col1, #T_be470_row25_col4, #T_be470_row25_col5, #T_be470_row26_col1, #T_be470_row26_col4, #T_be470_row26_col5, #T_be470_row27_col1, #T_be470_row27_col4, #T_be470_row27_col5, #T_be470_row28_col1, #T_be470_row28_col4, #T_be470_row28_col5, #T_be470_row29_col1, #T_be470_row29_col4, #T_be470_row29_col5, #T_be470_row30_col1, #T_be470_row30_col4, #T_be470_row30_col5, #T_be470_row31_col1, #T_be470_row31_col4, #T_be470_row31_col5, #T_be470_row32_col1, #T_be470_row32_col4, #T_be470_row32_col5, #T_be470_row33_col1, #T_be470_row33_col4, #T_be470_row33_col5, #T_be470_row34_col1, #T_be470_row34_col4, #T_be470_row34_col5, #T_be470_row35_col1, #T_be470_row35_col4, #T_be470_row35_col5, #T_be470_row36_col1, #T_be470_row36_col4, #T_be470_row36_col5 {\n  background-color: #fff5f0;\n  color: #000000;\n}\n#T_be470_row0_col2, #T_be470_row22_col2 {\n  background-color: #fedaca;\n  color: #000000;\n}\n#T_be470_row0_col3 {\n  background-color: #920a13;\n  color: #f1f1f1;\n}\n#T_be470_row0_col5, #T_be470_row1_col5, #T_be470_row2_col5, #T_be470_row3_col5, #T_be470_row4_col5, #T_be470_row5_col5, #T_be470_row6_col5, #T_be470_row32_col3 {\n  background-color: #fb694a;\n  color: #f1f1f1;\n}\n#T_be470_row1_col0, #T_be470_row13_col0 {\n  background-color: #fff4ee;\n  color: #000000;\n}\n#T_be470_row1_col2 {\n  background-color: #fff2ec;\n  color: #000000;\n}\n#T_be470_row1_col3 {\n  background-color: #ca181d;\n  color: #f1f1f1;\n}\n#T_be470_row2_col0 {\n  background-color: #fdd3c1;\n  color: #000000;\n}\n#T_be470_row2_col2, #T_be470_row8_col3, #T_be470_row15_col3 {\n  background-color: #fca78b;\n  color: #000000;\n}\n#T_be470_row2_col3 {\n  background-color: #6f020e;\n  color: #f1f1f1;\n}\n#T_be470_row3_col0, #T_be470_row8_col0 {\n  background-color: #fcc2aa;\n  color: #000000;\n}\n#T_be470_row3_col2 {\n  background-color: #fca588;\n  color: #000000;\n}\n#T_be470_row3_col3 {\n  background-color: #d72322;\n  color: #f1f1f1;\n}\n#T_be470_row4_col0, #T_be470_row28_col2 {\n  background-color: #fcb89e;\n  color: #000000;\n}\n#T_be470_row4_col2, #T_be470_row26_col3, #T_be470_row33_col3 {\n  background-color: #fb7050;\n  color: #f1f1f1;\n}\n#T_be470_row4_col3, #T_be470_row5_col2, #T_be470_row7_col5, #T_be470_row8_col5, #T_be470_row9_col5, #T_be470_row10_col5, #T_be470_row11_col5, #T_be470_row12_col5, #T_be470_row13_col5, #T_be470_row14_col5, #T_be470_row15_col5, #T_be470_row16_col5, #T_be470_row17_col5, #T_be470_row18_col5, #T_be470_row19_col5, #T_be470_row20_col5, #T_be470_row21_col5, #T_be470_row29_col0 {\n  background-color: #67000d;\n  color: #f1f1f1;\n}\n#T_be470_row5_col0 {\n  background-color: #f6583e;\n  color: #f1f1f1;\n}\n#T_be470_row5_col3 {\n  background-color: #69000d;\n  color: #f1f1f1;\n}\n#T_be470_row6_col0 {\n  background-color: #fcad90;\n  color: #000000;\n}\n#T_be470_row6_col2, #T_be470_row23_col3 {\n  background-color: #fc8565;\n  color: #f1f1f1;\n}\n#T_be470_row6_col3 {\n  background-color: #d32020;\n  color: #f1f1f1;\n}\n#T_be470_row7_col0, #T_be470_row9_col2, #T_be470_row19_col2 {\n  background-color: #fff0e9;\n  color: #000000;\n}\n#T_be470_row7_col2 {\n  background-color: #fff1ea;\n  color: #000000;\n}\n#T_be470_row7_col3 {\n  background-color: #fc9777;\n  color: #000000;\n}\n#T_be470_row8_col2, #T_be470_row24_col0 {\n  background-color: #fdd2bf;\n  color: #000000;\n}\n#T_be470_row9_col0, #T_be470_row12_col2, #T_be470_row15_col2 {\n  background-color: #fee4d8;\n  color: #000000;\n}\n#T_be470_row9_col3, #T_be470_row14_col0 {\n  background-color: #fff3ed;\n  color: #000000;\n}\n#T_be470_row10_col3 {\n  background-color: #fcb99f;\n  color: #000000;\n}\n#T_be470_row11_col0, #T_be470_row36_col2 {\n  background-color: #fee2d5;\n  color: #000000;\n}\n#T_be470_row11_col2, #T_be470_row21_col2, #T_be470_row27_col0, #T_be470_row36_col0 {\n  background-color: #fee6da;\n  color: #000000;\n}\n#T_be470_row11_col3, #T_be470_row20_col3 {\n  background-color: #fca082;\n  color: #000000;\n}\n#T_be470_row12_col0 {\n  background-color: #fdd0bc;\n  color: #000000;\n}\n#T_be470_row12_col3, #T_be470_row33_col0 {\n  background-color: #fdcbb6;\n  color: #000000;\n}\n#T_be470_row13_col2, #T_be470_row17_col0, #T_be470_row17_col2 {\n  background-color: #fff4ef;\n  color: #000000;\n}\n#T_be470_row13_col3 {\n  background-color: #fcc4ad;\n  color: #000000;\n}\n#T_be470_row15_col0 {\n  background-color: #fedecf;\n  color: #000000;\n}\n#T_be470_row16_col0 {\n  background-color: #e22e27;\n  color: #f1f1f1;\n}\n#T_be470_row16_col2 {\n  background-color: #f7593f;\n  color: #f1f1f1;\n}\n#T_be470_row16_col3 {\n  background-color: #fc9c7d;\n  color: #000000;\n}\n#T_be470_row17_col3 {\n  background-color: #fcb398;\n  color: #000000;\n}\n#T_be470_row18_col0 {\n  background-color: #fee5d8;\n  color: #000000;\n}\n#T_be470_row18_col3, #T_be470_row32_col0 {\n  background-color: #fc9373;\n  color: #000000;\n}\n#T_be470_row19_col0 {\n  background-color: #ffede5;\n  color: #000000;\n}\n#T_be470_row19_col3 {\n  background-color: #fdc7b2;\n  color: #000000;\n}\n#T_be470_row20_col2 {\n  background-color: #feeae0;\n  color: #000000;\n}\n#T_be470_row21_col0, #T_be470_row35_col0 {\n  background-color: #fee1d4;\n  color: #000000;\n}\n#T_be470_row21_col3 {\n  background-color: #fcaa8d;\n  color: #000000;\n}\n#T_be470_row22_col0, #T_be470_row24_col2, #T_be470_row26_col2 {\n  background-color: #fdd7c6;\n  color: #000000;\n}\n#T_be470_row22_col3, #T_be470_row27_col3 {\n  background-color: #fc8767;\n  color: #f1f1f1;\n}\n#T_be470_row23_col0 {\n  background-color: #b51318;\n  color: #f1f1f1;\n}\n#T_be470_row23_col2 {\n  background-color: #c9181d;\n  color: #f1f1f1;\n}\n#T_be470_row24_col3 {\n  background-color: #fc8a6a;\n  color: #f1f1f1;\n}\n#T_be470_row25_col0, #T_be470_row25_col2 {\n  background-color: #ffefe8;\n  color: #000000;\n}\n#T_be470_row25_col3 {\n  background-color: #fc8060;\n  color: #f1f1f1;\n}\n#T_be470_row26_col0 {\n  background-color: #fed8c7;\n  color: #000000;\n}\n#T_be470_row28_col0 {\n  background-color: #fcbea5;\n  color: #000000;\n}\n#T_be470_row28_col3 {\n  background-color: #f96346;\n  color: #f1f1f1;\n}\n#T_be470_row29_col2 {\n  background-color: #a60f15;\n  color: #f1f1f1;\n}\n#T_be470_row29_col3 {\n  background-color: #fc8f6f;\n  color: #000000;\n}\n#T_be470_row30_col0, #T_be470_row30_col2 {\n  background-color: #fff2eb;\n  color: #000000;\n}\n#T_be470_row30_col3 {\n  background-color: #fc7f5f;\n  color: #f1f1f1;\n}\n#T_be470_row31_col0 {\n  background-color: #fcbca2;\n  color: #000000;\n}\n#T_be470_row31_col2 {\n  background-color: #fcab8f;\n  color: #000000;\n}\n#T_be470_row31_col3 {\n  background-color: #f34935;\n  color: #f1f1f1;\n}\n#T_be470_row32_col2 {\n  background-color: #fc8d6d;\n  color: #f1f1f1;\n}\n#T_be470_row33_col2 {\n  background-color: #fdc9b3;\n  color: #000000;\n}\n#T_be470_row34_col0 {\n  background-color: #fedbcc;\n  color: #000000;\n}\n#T_be470_row34_col2 {\n  background-color: #fdcebb;\n  color: #000000;\n}\n#T_be470_row34_col3 {\n  background-color: #e83429;\n  color: #f1f1f1;\n}\n#T_be470_row35_col2 {\n  background-color: #fed9c9;\n  color: #000000;\n}\n#T_be470_row35_col3 {\n  background-color: #f14130;\n  color: #f1f1f1;\n}\n#T_be470_row36_col3 {\n  background-color: #ed392b;\n  color: #f1f1f1;\n}\n</style>\n<table id=\"T_be470_\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th class=\"col_heading level0 col0\" >confirmed</th>\n      <th class=\"col_heading level0 col1\" >recovered</th>\n      <th class=\"col_heading level0 col2\" >death</th>\n      <th class=\"col_heading level0 col3\" >Mortality</th>\n      <th class=\"col_heading level0 col4\" >Recovery</th>\n      <th class=\"col_heading level0 col5\" >Clusters</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_be470_level0_row0\" class=\"row_heading level0 row0\" >Connecticut</th>\n      <td id=\"T_be470_row0_col0\" class=\"data row0 col0\" >112412118.00</td>\n      <td id=\"T_be470_row0_col1\" class=\"data row0 col1\" >0.00</td>\n      <td id=\"T_be470_row0_col2\" class=\"data row0 col2\" >3436448.00</td>\n      <td id=\"T_be470_row0_col3\" class=\"data row0 col3\" >3.06</td>\n      <td id=\"T_be470_row0_col4\" class=\"data row0 col4\" >0.00</td>\n      <td id=\"T_be470_row0_col5\" class=\"data row0 col5\" >1.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row1\" class=\"row_heading level0 row1\" >District of Columbia</th>\n      <td id=\"T_be470_row1_col0\" class=\"data row1 col0\" >17657996.00</td>\n      <td id=\"T_be470_row1_col1\" class=\"data row1 col1\" >0.00</td>\n      <td id=\"T_be470_row1_col2\" class=\"data row1 col2\" >460675.00</td>\n      <td id=\"T_be470_row1_col3\" class=\"data row1 col3\" >2.61</td>\n      <td id=\"T_be470_row1_col4\" class=\"data row1 col4\" >0.00</td>\n      <td id=\"T_be470_row1_col5\" class=\"data row1 col5\" >1.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row2\" class=\"row_heading level0 row2\" >Massachusetts</th>\n      <td id=\"T_be470_row2_col0\" class=\"data row2 col0\" >221315696.00</td>\n      <td id=\"T_be470_row2_col1\" class=\"data row2 col1\" >0.00</td>\n      <td id=\"T_be470_row2_col2\" class=\"data row2 col2\" >7205123.00</td>\n      <td id=\"T_be470_row2_col3\" class=\"data row2 col3\" >3.26</td>\n      <td id=\"T_be470_row2_col4\" class=\"data row2 col4\" >0.00</td>\n      <td id=\"T_be470_row2_col5\" class=\"data row2 col5\" >1.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row3\" class=\"row_heading level0 row3\" >Michigan</th>\n      <td id=\"T_be470_row3_col0\" class=\"data row3 col0\" >295628347.00</td>\n      <td id=\"T_be470_row3_col1\" class=\"data row3 col1\" >0.00</td>\n      <td id=\"T_be470_row3_col2\" class=\"data row3 col2\" >7365116.00</td>\n      <td id=\"T_be470_row3_col3\" class=\"data row3 col3\" >2.49</td>\n      <td id=\"T_be470_row3_col4\" class=\"data row3 col4\" >0.00</td>\n      <td id=\"T_be470_row3_col5\" class=\"data row3 col5\" >1.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row4\" class=\"row_heading level0 row4\" >New Jersey</th>\n      <td id=\"T_be470_row4_col0\" class=\"data row4 col0\" >334801563.00</td>\n      <td id=\"T_be470_row4_col1\" class=\"data row4 col1\" >0.00</td>\n      <td id=\"T_be470_row4_col2\" class=\"data row4 col2\" >11054685.00</td>\n      <td id=\"T_be470_row4_col3\" class=\"data row4 col3\" >3.30</td>\n      <td id=\"T_be470_row4_col4\" class=\"data row4 col4\" >0.00</td>\n      <td id=\"T_be470_row4_col5\" class=\"data row4 col5\" >1.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row5\" class=\"row_heading level0 row5\" >New York</th>\n      <td id=\"T_be470_row5_col0\" class=\"data row5 col0\" >696697335.00</td>\n      <td id=\"T_be470_row5_col1\" class=\"data row5 col1\" >0.00</td>\n      <td id=\"T_be470_row5_col2\" class=\"data row5 col2\" >22868829.00</td>\n      <td id=\"T_be470_row5_col3\" class=\"data row5 col3\" >3.28</td>\n      <td id=\"T_be470_row5_col4\" class=\"data row5 col4\" >0.00</td>\n      <td id=\"T_be470_row5_col5\" class=\"data row5 col5\" >1.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row6\" class=\"row_heading level0 row6\" >Pennsylvania</th>\n      <td id=\"T_be470_row6_col0\" class=\"data row6 col0\" >378669082.00</td>\n      <td id=\"T_be470_row6_col1\" class=\"data row6 col1\" >0.00</td>\n      <td id=\"T_be470_row6_col2\" class=\"data row6 col2\" >9564450.00</td>\n      <td id=\"T_be470_row6_col3\" class=\"data row6 col3\" >2.53</td>\n      <td id=\"T_be470_row6_col4\" class=\"data row6 col4\" >0.00</td>\n      <td id=\"T_be470_row6_col5\" class=\"data row6 col5\" >1.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row7\" class=\"row_heading level0 row7\" >South Dakota</th>\n      <td id=\"T_be470_row7_col0\" class=\"data row7 col0\" >43223765.00</td>\n      <td id=\"T_be470_row7_col1\" class=\"data row7 col1\" >0.00</td>\n      <td id=\"T_be470_row7_col2\" class=\"data row7 col2\" >658377.00</td>\n      <td id=\"T_be470_row7_col3\" class=\"data row7 col3\" >1.52</td>\n      <td id=\"T_be470_row7_col4\" class=\"data row7 col4\" >0.00</td>\n      <td id=\"T_be470_row7_col5\" class=\"data row7 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row8\" class=\"row_heading level0 row8\" >Tennessee</th>\n      <td id=\"T_be470_row8_col0\" class=\"data row8 col0\" >294805484.00</td>\n      <td id=\"T_be470_row8_col1\" class=\"data row8 col1\" >0.00</td>\n      <td id=\"T_be470_row8_col2\" class=\"data row8 col2\" >4060985.00</td>\n      <td id=\"T_be470_row8_col3\" class=\"data row8 col3\" >1.38</td>\n      <td id=\"T_be470_row8_col4\" class=\"data row8 col4\" >0.00</td>\n      <td id=\"T_be470_row8_col5\" class=\"data row8 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row9\" class=\"row_heading level0 row9\" >Utah</th>\n      <td id=\"T_be470_row9_col0\" class=\"data row9 col0\" >138789349.00</td>\n      <td id=\"T_be470_row9_col1\" class=\"data row9 col1\" >0.00</td>\n      <td id=\"T_be470_row9_col2\" class=\"data row9 col2\" >762311.00</td>\n      <td id=\"T_be470_row9_col3\" class=\"data row9 col3\" >0.55</td>\n      <td id=\"T_be470_row9_col4\" class=\"data row9 col4\" >0.00</td>\n      <td id=\"T_be470_row9_col5\" class=\"data row9 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row10\" class=\"row_heading level0 row10\" >Vermont</th>\n      <td id=\"T_be470_row10_col0\" class=\"data row10 col0\" >7245172.00</td>\n      <td id=\"T_be470_row10_col1\" class=\"data row10 col1\" >0.00</td>\n      <td id=\"T_be470_row10_col2\" class=\"data row10 col2\" >88479.00</td>\n      <td id=\"T_be470_row10_col3\" class=\"data row10 col3\" >1.22</td>\n      <td id=\"T_be470_row10_col4\" class=\"data row10 col4\" >0.00</td>\n      <td id=\"T_be470_row10_col5\" class=\"data row10 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row11\" class=\"row_heading level0 row11\" >Washington</th>\n      <td id=\"T_be470_row11_col0\" class=\"data row11 col0\" >151493193.00</td>\n      <td id=\"T_be470_row11_col1\" class=\"data row11 col1\" >0.00</td>\n      <td id=\"T_be470_row11_col2\" class=\"data row11 col2\" >2177587.00</td>\n      <td id=\"T_be470_row11_col3\" class=\"data row11 col3\" >1.44</td>\n      <td id=\"T_be470_row11_col4\" class=\"data row11 col4\" >0.00</td>\n      <td id=\"T_be470_row11_col5\" class=\"data row11 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row12\" class=\"row_heading level0 row12\" >Wisconsin</th>\n      <td id=\"T_be470_row12_col0\" class=\"data row12 col0\" >235528444.00</td>\n      <td id=\"T_be470_row12_col1\" class=\"data row12 col1\" >0.00</td>\n      <td id=\"T_be470_row12_col2\" class=\"data row12 col2\" >2486406.00</td>\n      <td id=\"T_be470_row12_col3\" class=\"data row12 col3\" >1.06</td>\n      <td id=\"T_be470_row12_col4\" class=\"data row12 col4\" >0.00</td>\n      <td id=\"T_be470_row12_col5\" class=\"data row12 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row13\" class=\"row_heading level0 row13\" >Wyoming</th>\n      <td id=\"T_be470_row13_col0\" class=\"data row13 col0\" >21441507.00</td>\n      <td id=\"T_be470_row13_col1\" class=\"data row13 col1\" >0.00</td>\n      <td id=\"T_be470_row13_col2\" class=\"data row13 col2\" >241162.00</td>\n      <td id=\"T_be470_row13_col3\" class=\"data row13 col3\" >1.12</td>\n      <td id=\"T_be470_row13_col4\" class=\"data row13 col4\" >0.00</td>\n      <td id=\"T_be470_row13_col5\" class=\"data row13 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row14\" class=\"row_heading level0 row14\" >Alaska</th>\n      <td id=\"T_be470_row14_col0\" class=\"data row14 col0\" >23841592.00</td>\n      <td id=\"T_be470_row14_col1\" class=\"data row14 col1\" >0.00</td>\n      <td id=\"T_be470_row14_col2\" class=\"data row14 col2\" >121298.00</td>\n      <td id=\"T_be470_row14_col3\" class=\"data row14 col3\" >0.51</td>\n      <td id=\"T_be470_row14_col4\" class=\"data row14 col4\" >0.00</td>\n      <td id=\"T_be470_row14_col5\" class=\"data row14 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row15\" class=\"row_heading level0 row15\" >Colorado</th>\n      <td id=\"T_be470_row15_col0\" class=\"data row15 col0\" >177585551.00</td>\n      <td id=\"T_be470_row15_col1\" class=\"data row15 col1\" >0.00</td>\n      <td id=\"T_be470_row15_col2\" class=\"data row15 col2\" >2445856.00</td>\n      <td id=\"T_be470_row15_col3\" class=\"data row15 col3\" >1.38</td>\n      <td id=\"T_be470_row15_col4\" class=\"data row15 col4\" >0.00</td>\n      <td id=\"T_be470_row15_col5\" class=\"data row15 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row16\" class=\"row_heading level0 row16\" >Florida</th>\n      <td id=\"T_be470_row16_col0\" class=\"data row16 col0\" >848226468.00</td>\n      <td id=\"T_be470_row16_col1\" class=\"data row16 col1\" >0.00</td>\n      <td id=\"T_be470_row16_col2\" class=\"data row16 col2\" >12478196.00</td>\n      <td id=\"T_be470_row16_col3\" class=\"data row16 col3\" >1.47</td>\n      <td id=\"T_be470_row16_col4\" class=\"data row16 col4\" >0.00</td>\n      <td id=\"T_be470_row16_col5\" class=\"data row16 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row17\" class=\"row_heading level0 row17\" >Hawaii</th>\n      <td id=\"T_be470_row17_col0\" class=\"data row17 col0\" >14144001.00</td>\n      <td id=\"T_be470_row17_col1\" class=\"data row17 col1\" >0.00</td>\n      <td id=\"T_be470_row17_col2\" class=\"data row17 col2\" >180921.00</td>\n      <td id=\"T_be470_row17_col3\" class=\"data row17 col3\" >1.28</td>\n      <td id=\"T_be470_row17_col4\" class=\"data row17 col4\" >0.00</td>\n      <td id=\"T_be470_row17_col5\" class=\"data row17 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row18\" class=\"row_heading level0 row18\" >Iowa</th>\n      <td id=\"T_be470_row18_col0\" class=\"data row18 col0\" >131506421.00</td>\n      <td id=\"T_be470_row18_col1\" class=\"data row18 col1\" >0.00</td>\n      <td id=\"T_be470_row18_col2\" class=\"data row18 col2\" >2033449.00</td>\n      <td id=\"T_be470_row18_col3\" class=\"data row18 col3\" >1.55</td>\n      <td id=\"T_be470_row18_col4\" class=\"data row18 col4\" >0.00</td>\n      <td id=\"T_be470_row18_col5\" class=\"data row18 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row19\" class=\"row_heading level0 row19\" >Idaho</th>\n      <td id=\"T_be470_row19_col0\" class=\"data row19 col0\" >68496689.00</td>\n      <td id=\"T_be470_row19_col1\" class=\"data row19 col1\" >0.00</td>\n      <td id=\"T_be470_row19_col2\" class=\"data row19 col2\" >746126.00</td>\n      <td id=\"T_be470_row19_col3\" class=\"data row19 col3\" >1.09</td>\n      <td id=\"T_be470_row19_col4\" class=\"data row19 col4\" >0.00</td>\n      <td id=\"T_be470_row19_col5\" class=\"data row19 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row20\" class=\"row_heading level0 row20\" >Kansas</th>\n      <td id=\"T_be470_row20_col0\" class=\"data row20 col0\" >110929450.00</td>\n      <td id=\"T_be470_row20_col1\" class=\"data row20 col1\" >0.00</td>\n      <td id=\"T_be470_row20_col2\" class=\"data row20 col2\" >1602300.00</td>\n      <td id=\"T_be470_row20_col3\" class=\"data row20 col3\" >1.44</td>\n      <td id=\"T_be470_row20_col4\" class=\"data row20 col4\" >0.00</td>\n      <td id=\"T_be470_row20_col5\" class=\"data row20 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row21\" class=\"row_heading level0 row21\" >Kentucky</th>\n      <td id=\"T_be470_row21_col0\" class=\"data row21 col0\" >159177697.00</td>\n      <td id=\"T_be470_row21_col1\" class=\"data row21 col1\" >0.00</td>\n      <td id=\"T_be470_row21_col2\" class=\"data row21 col2\" >2161617.00</td>\n      <td id=\"T_be470_row21_col3\" class=\"data row21 col3\" >1.36</td>\n      <td id=\"T_be470_row21_col4\" class=\"data row21 col4\" >0.00</td>\n      <td id=\"T_be470_row21_col5\" class=\"data row21 col5\" >2.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row22\" class=\"row_heading level0 row22\" >South Carolina</th>\n      <td id=\"T_be470_row22_col0\" class=\"data row22 col0\" >207059600.00</td>\n      <td id=\"T_be470_row22_col1\" class=\"data row22 col1\" >0.00</td>\n      <td id=\"T_be470_row22_col2\" class=\"data row22 col2\" >3424848.00</td>\n      <td id=\"T_be470_row22_col3\" class=\"data row22 col3\" >1.65</td>\n      <td id=\"T_be470_row22_col4\" class=\"data row22 col4\" >0.00</td>\n      <td id=\"T_be470_row22_col5\" class=\"data row22 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row23\" class=\"row_heading level0 row23\" >Texas</th>\n      <td id=\"T_be470_row23_col0\" class=\"data row23 col0\" >1038325285.00</td>\n      <td id=\"T_be470_row23_col1\" class=\"data row23 col1\" >0.00</td>\n      <td id=\"T_be470_row23_col2\" class=\"data row23 col2\" >17348402.00</td>\n      <td id=\"T_be470_row23_col3\" class=\"data row23 col3\" >1.67</td>\n      <td id=\"T_be470_row23_col4\" class=\"data row23 col4\" >0.00</td>\n      <td id=\"T_be470_row23_col5\" class=\"data row23 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row24\" class=\"row_heading level0 row24\" >Virginia</th>\n      <td id=\"T_be470_row24_col0\" class=\"data row24 col0\" >227526385.00</td>\n      <td id=\"T_be470_row24_col1\" class=\"data row24 col1\" >0.00</td>\n      <td id=\"T_be470_row24_col2\" class=\"data row24 col2\" >3698877.00</td>\n      <td id=\"T_be470_row24_col3\" class=\"data row24 col3\" >1.63</td>\n      <td id=\"T_be470_row24_col4\" class=\"data row24 col4\" >0.00</td>\n      <td id=\"T_be470_row24_col5\" class=\"data row24 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row25\" class=\"row_heading level0 row25\" >West Virginia</th>\n      <td id=\"T_be470_row25_col0\" class=\"data row25 col0\" >52832389.00</td>\n      <td id=\"T_be470_row25_col1\" class=\"data row25 col1\" >0.00</td>\n      <td id=\"T_be470_row25_col2\" class=\"data row25 col2\" >903528.00</td>\n      <td id=\"T_be470_row25_col3\" class=\"data row25 col3\" >1.71</td>\n      <td id=\"T_be470_row25_col4\" class=\"data row25 col4\" >0.00</td>\n      <td id=\"T_be470_row25_col5\" class=\"data row25 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row26\" class=\"row_heading level0 row26\" >Alabama</th>\n      <td id=\"T_be470_row26_col0\" class=\"data row26 col0\" >200323493.00</td>\n      <td id=\"T_be470_row26_col1\" class=\"data row26 col1\" >0.00</td>\n      <td id=\"T_be470_row26_col2\" class=\"data row26 col2\" >3723145.00</td>\n      <td id=\"T_be470_row26_col3\" class=\"data row26 col3\" >1.86</td>\n      <td id=\"T_be470_row26_col4\" class=\"data row26 col4\" >0.00</td>\n      <td id=\"T_be470_row26_col5\" class=\"data row26 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row27\" class=\"row_heading level0 row27\" >Arkansas</th>\n      <td id=\"T_be470_row27_col0\" class=\"data row27 col0\" >124239415.00</td>\n      <td id=\"T_be470_row27_col1\" class=\"data row27 col1\" >0.00</td>\n      <td id=\"T_be470_row27_col2\" class=\"data row27 col2\" >2041981.00</td>\n      <td id=\"T_be470_row27_col3\" class=\"data row27 col3\" >1.64</td>\n      <td id=\"T_be470_row27_col4\" class=\"data row27 col4\" >0.00</td>\n      <td id=\"T_be470_row27_col5\" class=\"data row27 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row28\" class=\"row_heading level0 row28\" >Arizona</th>\n      <td id=\"T_be470_row28_col0\" class=\"data row28 col0\" >307229820.00</td>\n      <td id=\"T_be470_row28_col1\" class=\"data row28 col1\" >0.00</td>\n      <td id=\"T_be470_row28_col2\" class=\"data row28 col2\" >6015311.00</td>\n      <td id=\"T_be470_row28_col3\" class=\"data row28 col3\" >1.96</td>\n      <td id=\"T_be470_row28_col4\" class=\"data row28 col4\" >0.00</td>\n      <td id=\"T_be470_row28_col5\" class=\"data row28 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row29\" class=\"row_heading level0 row29\" >California</th>\n      <td id=\"T_be470_row29_col0\" class=\"data row29 col0\" >1262016103.00</td>\n      <td id=\"T_be470_row29_col1\" class=\"data row29 col1\" >0.00</td>\n      <td id=\"T_be470_row29_col2\" class=\"data row29 col2\" >19931034.00</td>\n      <td id=\"T_be470_row29_col3\" class=\"data row29 col3\" >1.58</td>\n      <td id=\"T_be470_row29_col4\" class=\"data row29 col4\" >0.00</td>\n      <td id=\"T_be470_row29_col5\" class=\"data row29 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row30\" class=\"row_heading level0 row30\" >Delaware</th>\n      <td id=\"T_be470_row30_col0\" class=\"data row30 col0\" >35547897.00</td>\n      <td id=\"T_be470_row30_col1\" class=\"data row30 col1\" >0.00</td>\n      <td id=\"T_be470_row30_col2\" class=\"data row30 col2\" >615046.00</td>\n      <td id=\"T_be470_row30_col3\" class=\"data row30 col3\" >1.73</td>\n      <td id=\"T_be470_row30_col4\" class=\"data row30 col4\" >0.00</td>\n      <td id=\"T_be470_row30_col5\" class=\"data row30 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row31\" class=\"row_heading level0 row31\" >Georgia</th>\n      <td id=\"T_be470_row31_col0\" class=\"data row31 col0\" >319233753.00</td>\n      <td id=\"T_be470_row31_col1\" class=\"data row31 col1\" >0.00</td>\n      <td id=\"T_be470_row31_col2\" class=\"data row31 col2\" >6882707.00</td>\n      <td id=\"T_be470_row31_col3\" class=\"data row31 col3\" >2.16</td>\n      <td id=\"T_be470_row31_col4\" class=\"data row31 col4\" >0.00</td>\n      <td id=\"T_be470_row31_col5\" class=\"data row31 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row32\" class=\"row_heading level0 row32\" >Illinois</th>\n      <td id=\"T_be470_row32_col0\" class=\"data row32 col0\" >476018159.00</td>\n      <td id=\"T_be470_row32_col1\" class=\"data row32 col1\" >0.00</td>\n      <td id=\"T_be470_row32_col2\" class=\"data row32 col2\" >9074100.00</td>\n      <td id=\"T_be470_row32_col3\" class=\"data row32 col3\" >1.91</td>\n      <td id=\"T_be470_row32_col4\" class=\"data row32 col4\" >0.00</td>\n      <td id=\"T_be470_row32_col5\" class=\"data row32 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row33\" class=\"row_heading level0 row33\" >Indiana</th>\n      <td id=\"T_be470_row33_col0\" class=\"data row33 col0\" >257025658.00</td>\n      <td id=\"T_be470_row33_col1\" class=\"data row33 col1\" >0.00</td>\n      <td id=\"T_be470_row33_col2\" class=\"data row33 col2\" >4783841.00</td>\n      <td id=\"T_be470_row33_col3\" class=\"data row33 col3\" >1.86</td>\n      <td id=\"T_be470_row33_col4\" class=\"data row33 col4\" >0.00</td>\n      <td id=\"T_be470_row33_col5\" class=\"data row33 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row34\" class=\"row_heading level0 row34\" >Louisiana</th>\n      <td id=\"T_be470_row34_col0\" class=\"data row34 col0\" >185972187.00</td>\n      <td id=\"T_be470_row34_col1\" class=\"data row34 col1\" >0.00</td>\n      <td id=\"T_be470_row34_col2\" class=\"data row34 col2\" >4319891.00</td>\n      <td id=\"T_be470_row34_col3\" class=\"data row34 col3\" >2.32</td>\n      <td id=\"T_be470_row34_col4\" class=\"data row34 col4\" >0.00</td>\n      <td id=\"T_be470_row34_col5\" class=\"data row34 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row35\" class=\"row_heading level0 row35\" >Maryland</th>\n      <td id=\"T_be470_row35_col0\" class=\"data row35 col0\" >158010274.00</td>\n      <td id=\"T_be470_row35_col1\" class=\"data row35 col1\" >0.00</td>\n      <td id=\"T_be470_row35_col2\" class=\"data row35 col2\" >3491665.00</td>\n      <td id=\"T_be470_row35_col3\" class=\"data row35 col3\" >2.21</td>\n      <td id=\"T_be470_row35_col4\" class=\"data row35 col4\" >0.00</td>\n      <td id=\"T_be470_row35_col5\" class=\"data row35 col5\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_be470_level0_row36\" class=\"row_heading level0 row36\" >Mississippi</th>\n      <td id=\"T_be470_row36_col0\" class=\"data row36 col0\" >121279152.00</td>\n      <td id=\"T_be470_row36_col1\" class=\"data row36 col1\" >0.00</td>\n      <td id=\"T_be470_row36_col2\" class=\"data row36 col2\" >2749046.00</td>\n      <td id=\"T_be470_row36_col3\" class=\"data row36 col3\" >2.27</td>\n      <td id=\"T_be470_row36_col4\" class=\"data row36 col4\" >0.00</td>\n      <td id=\"T_be470_row36_col5\" class=\"data row36 col5\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#!pip install plotly\n",
    "try:\n",
    "    # https://stackoverflow.com/questions/57105747/modulenotfounderror-no-module-named-plotly-graph-objects/57112843\n",
    "    #     import plotly.graph_objects as go\n",
    "    #     import plotly.express as px\n",
    "    import plotly.express as px\n",
    "    import plotly.graph_objects as go\n",
    "except ImportError as e:\n",
    "    from plotly import graph_objs as go\n",
    "    from plotly import express as px\n",
    "# import plotly.express as px\n",
    "# import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from datetime import timedelta\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score,silhouette_samples\n",
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.api import Holt,SimpleExpSmoothing,ExponentialSmoothing\n",
    "# from fbprophet import Prophet\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "# !pip install pyramid-arima\n",
    "# from pyramid.arima import auto_arima\n",
    "std=StandardScaler()\n",
    "#pd.set_option('display.float_format', lambda x: '%.6f' % x)\n",
    "# out\n",
    "# filename=r\"G:\\file\\学校\\可视化\\大作业\\COVID-19\\COVID-19-Data-master\\US\\County_level_summary\\US_County_summary_covid19_confirmed_transpose.csv\"\n",
    "\n",
    "state_filename_base=r\"G:\\file\\学校\\可视化\\大作业\\COVID-19\\COVID-19-Data-master\\US\\State_level_summary\\US_State_summary_covid19_{}_trpo.xlsx\"\n",
    "# state_filename_base=r\"COVID-19-Data-master\\US\\State_level_summary\\US_State_summary_covid19_{}_trpo.xlsx\"\n",
    "# state_filename_base=r\"COVID-19-Data-master/US/State_level_summary/US_State_summary_covid19_{}_trpo.xlsx\"\n",
    "\n",
    "\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "\n",
    "def get_sum(type_name):\n",
    "\n",
    "    df = pd.read_excel(state_filename_base.format(type_name))\n",
    "    # pd 每一列 求和\n",
    "    df_sum = df.sum()\n",
    "    # print(\"df_sum\")\n",
    "    # print(df_sum)\n",
    "    return df_sum\n",
    "\n",
    "\n",
    "def get_3type_df():\n",
    "    df_confirmed = pd.read_excel(state_filename_base.format(\"confirmed\"))\n",
    "    df_recovered = pd.read_excel(state_filename_base.format(\"recovered\"))\n",
    "    df_death = pd.read_excel(state_filename_base.format(\"death\"))\n",
    "    # df=\n",
    "\n",
    "    df=pd.DataFrame({})\n",
    "    df[\"confirmed\"]=get_sum(\"confirmed\")\n",
    "    df[\"recovered\"]=get_sum(\"recovered\")\n",
    "    df[\"death\"]=get_sum(\"death\")\n",
    "    # print(\"df_death\")\n",
    "    # print(df_death)\n",
    "    # print(\"df_death.shape\")\n",
    "    # print(df_death.shape)\n",
    "    return df\n",
    "covid=get_3type_df()\n",
    "\n",
    "confirmed_col=\"confirmed\"\n",
    "recovered_col=\"recovered\"\n",
    "death_col=\"death\"\n",
    "\n",
    "datewise=covid\n",
    "# yy=datewise[confirmed_col]-datewise[recovered_col]-datewise[death_col]\n",
    "# print(\"yy\")\n",
    "# print(yy)\n",
    "# print(\"datewise.index\")\n",
    "# print(datewise.index)\n",
    "# 总的 和\n",
    "# 根据不同的 couty\n",
    "# 确诊的 - 治愈的 - 死亡的\n",
    "# 就是现在还在患病的\n",
    "# 分配  Distribution 分布\n",
    "countrywise=datewise\n",
    "# countrywise[\"Mortality\"]=(countrywise[\"Deaths\"]/countrywise[\"Confirmed\"])*100\n",
    "# countrywise[\"Recovery\"]=(countrywise[\"Recovered\"]/countrywise[\"Confirmed\"])*100\n",
    "\n",
    "countrywise[\"Mortality\"]=(countrywise[death_col]/countrywise[confirmed_col])*100\n",
    "countrywise[\"Recovery\"]=(countrywise[recovered_col]/countrywise[confirmed_col])*100\n",
    "\n",
    "# fig=px.bar(x=datewise.index,y=datewise[confirmed_col]-datewise[recovered_col]-datewise[death_col])\n",
    "# fig.update_layout(title=\"Distribution of Number of Active Cases 累计患病的分布(各个县)\",\n",
    "#                   xaxis_title=\"县\",yaxis_title=\"Number of Cases 患病的个数\",)\n",
    "# # xaxis_title=\"Date\",yaxis_title=\"Number of Cases\",\n",
    "# fig.show()\n",
    "\n",
    "# 正在患病的分布(各个县)\"\n",
    "# 为什么没有显示呢\n",
    "\n",
    "X=countrywise[[\"Mortality\",\"Recovery\"]]\n",
    "# 死亡率 Mortality\n",
    "#Standard Scaling since K-Means Clustering is a distance based alogrithm\n",
    "#标准缩放，因为K-均值聚类是一种基于距离的算法\n",
    "X=std.fit_transform(X)\n",
    "\n",
    "wcss=[]\n",
    "sil=[]\n",
    "for i in range(2,11):\n",
    "    clf=KMeans(n_clusters=i,init='k-means++',random_state=42)\n",
    "    clf.fit(X)\n",
    "    labels=clf.labels_\n",
    "    centroids=clf.cluster_centers_\n",
    "    sil.append(silhouette_score(X, labels, metric='euclidean'))\n",
    "    wcss.append(clf.inertia_)\n",
    "\n",
    "def example():\n",
    "\n",
    "    x=np.arange(2,11)\n",
    "    plt.figure(figsize=(10,5))\n",
    "    plt.plot(x,wcss,marker='o')\n",
    "    plt.xlabel(\"Number of Clusters 集群的个数 \")\n",
    "    # 集群;群集;\n",
    "    plt.ylabel(\"Within Cluster Sum of Squares (WCSS) 簇内平方和\")\n",
    "    # 簇内平方和（WCSS）\n",
    "    plt.title(\"Elbow Method 肘部法则\")\n",
    "    # –Elbow Method和轮廓...\n",
    "    # 肘部法则\n",
    "    plt.show()\n",
    "\n",
    "# countrywise[\"Mortality\"]=(countrywise[\"Deaths\"]/countrywise[\"Confirmed\"])*100\n",
    "# countrywise[\"Recovery\"]=(countrywise[\"Recovered\"]/countrywise[\"Confirmed\"])*100\n",
    "\n",
    "import scipy.cluster.hierarchy as sch\n",
    "# 等级制度(尤指社会或组织); 统治集团; 层次体系; hierarchy\n",
    "#\n",
    "\n",
    "def HierarchicalClusteringTest():\n",
    "    plt.figure(figsize=(20,15))\n",
    "    # dendrogram 系统树图（一种表示亲缘关系的树状图解）;\n",
    "    # 连接; 联系; 链环; 连锁; 联动装置; linkage\n",
    "\n",
    "    dendogram=sch.dendrogram(sch.linkage(X, method  = \"ward\"))\n",
    "    # dendogram.\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "clf_final=KMeans(n_clusters=3,init='k-means++',random_state=6)\n",
    "clf_final.fit(X)\n",
    "\n",
    "countrywise[\"Clusters\"]=clf_final.predict(X)\n",
    "\n",
    "cluster_summary=pd.concat([countrywise[countrywise[\"Clusters\"]==1].head(15),\n",
    "                           countrywise[countrywise[\"Clusters\"]==2].head(15),\n",
    "                           countrywise[countrywise[\"Clusters\"]==0].head(15)])\n",
    "cluster_summary.style.background_gradient(cmap='Reds').format(\"{:.2f}\")\n",
    "# plt.show()\n",
    "# print(\"cluster_summary\")\n",
    "# print(cluster_summary)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                       confirmed  recovered     death  Mortality  Recovery  \\\n",
       "Connecticut            112412118          0   3436448   3.057008       0.0   \n",
       "District of Columbia    17657996          0    460675   2.608875       0.0   \n",
       "Massachusetts          221315696          0   7205123   3.255586       0.0   \n",
       "Michigan               295628347          0   7365116   2.491343       0.0   \n",
       "New Jersey             334801563          0  11054685   3.301862       0.0   \n",
       "New York               696697335          0  22868829   3.282463       0.0   \n",
       "Pennsylvania           378669082          0   9564450   2.525807       0.0   \n",
       "South Dakota            43223765          0    658377   1.523183       0.0   \n",
       "Tennessee              294805484          0   4060985   1.377513       0.0   \n",
       "Utah                   138789349          0    762311   0.549258       0.0   \n",
       "Vermont                  7245172          0     88479   1.221213       0.0   \n",
       "Washington             151493193          0   2177587   1.437416       0.0   \n",
       "Wisconsin              235528444          0   2486406   1.055671       0.0   \n",
       "Wyoming                 21441507          0    241162   1.124744       0.0   \n",
       "Alaska                  23841592          0    121298   0.508766       0.0   \n",
       "Colorado               177585551          0   2445856   1.377283       0.0   \n",
       "Florida                848226468          0  12478196   1.471093       0.0   \n",
       "Hawaii                  14144001          0    180921   1.279136       0.0   \n",
       "Iowa                   131506421          0   2033449   1.546274       0.0   \n",
       "Idaho                   68496689          0    746126   1.089288       0.0   \n",
       "Kansas                 110929450          0   1602300   1.444432       0.0   \n",
       "Kentucky               159177697          0   2161617   1.357990       0.0   \n",
       "South Carolina         207059600          0   3424848   1.654040       0.0   \n",
       "Texas                 1038325285          0  17348402   1.670806       0.0   \n",
       "Virginia               227526385          0   3698877   1.625691       0.0   \n",
       "West Virginia           52832389          0    903528   1.710178       0.0   \n",
       "Alabama                200323493          0   3723145   1.858566       0.0   \n",
       "Arkansas               124239415          0   2041981   1.643585       0.0   \n",
       "Arizona                307229820          0   6015311   1.957919       0.0   \n",
       "California            1262016103          0  19931034   1.579301       0.0   \n",
       "Delaware                35547897          0    615046   1.730190       0.0   \n",
       "Georgia                319233753          0   6882707   2.156009       0.0   \n",
       "Illinois               476018159          0   9074100   1.906251       0.0   \n",
       "Indiana                257025658          0   4783841   1.861231       0.0   \n",
       "Louisiana              185972187          0   4319891   2.322869       0.0   \n",
       "Maryland               158010274          0   3491665   2.209771       0.0   \n",
       "Mississippi            121279152          0   2749046   2.266709       0.0   \n",
       "\n",
       "                      Clusters  \n",
       "Connecticut                  1  \n",
       "District of Columbia         1  \n",
       "Massachusetts                1  \n",
       "Michigan                     1  \n",
       "New Jersey                   1  \n",
       "New York                     1  \n",
       "Pennsylvania                 1  \n",
       "South Dakota                 2  \n",
       "Tennessee                    2  \n",
       "Utah                         2  \n",
       "Vermont                      2  \n",
       "Washington                   2  \n",
       "Wisconsin                    2  \n",
       "Wyoming                      2  \n",
       "Alaska                       2  \n",
       "Colorado                     2  \n",
       "Florida                      2  \n",
       "Hawaii                       2  \n",
       "Iowa                         2  \n",
       "Idaho                        2  \n",
       "Kansas                       2  \n",
       "Kentucky                     2  \n",
       "South Carolina               0  \n",
       "Texas                        0  \n",
       "Virginia                     0  \n",
       "West Virginia                0  \n",
       "Alabama                      0  \n",
       "Arkansas                     0  \n",
       "Arizona                      0  \n",
       "California                   0  \n",
       "Delaware                     0  \n",
       "Georgia                      0  \n",
       "Illinois                     0  \n",
       "Indiana                      0  \n",
       "Louisiana                    0  \n",
       "Maryland                     0  \n",
       "Mississippi                  0  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>confirmed</th>\n      <th>recovered</th>\n      <th>death</th>\n      <th>Mortality</th>\n      <th>Recovery</th>\n      <th>Clusters</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Connecticut</th>\n      <td>112412118</td>\n      <td>0</td>\n      <td>3436448</td>\n      <td>3.057008</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>District of Columbia</th>\n      <td>17657996</td>\n      <td>0</td>\n      <td>460675</td>\n      <td>2.608875</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Massachusetts</th>\n      <td>221315696</td>\n      <td>0</td>\n      <td>7205123</td>\n      <td>3.255586</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Michigan</th>\n      <td>295628347</td>\n      <td>0</td>\n      <td>7365116</td>\n      <td>2.491343</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>New Jersey</th>\n      <td>334801563</td>\n      <td>0</td>\n      <td>11054685</td>\n      <td>3.301862</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>New York</th>\n      <td>696697335</td>\n      <td>0</td>\n      <td>22868829</td>\n      <td>3.282463</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Pennsylvania</th>\n      <td>378669082</td>\n      <td>0</td>\n      <td>9564450</td>\n      <td>2.525807</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>South Dakota</th>\n      <td>43223765</td>\n      <td>0</td>\n      <td>658377</td>\n      <td>1.523183</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Tennessee</th>\n      <td>294805484</td>\n      <td>0</td>\n      <td>4060985</td>\n      <td>1.377513</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Utah</th>\n      <td>138789349</td>\n      <td>0</td>\n      <td>762311</td>\n      <td>0.549258</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Vermont</th>\n      <td>7245172</td>\n      <td>0</td>\n      <td>88479</td>\n      <td>1.221213</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Washington</th>\n      <td>151493193</td>\n      <td>0</td>\n      <td>2177587</td>\n      <td>1.437416</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Wisconsin</th>\n      <td>235528444</td>\n      <td>0</td>\n      <td>2486406</td>\n      <td>1.055671</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Wyoming</th>\n      <td>21441507</td>\n      <td>0</td>\n      <td>241162</td>\n      <td>1.124744</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Alaska</th>\n      <td>23841592</td>\n      <td>0</td>\n      <td>121298</td>\n      <td>0.508766</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Colorado</th>\n      <td>177585551</td>\n      <td>0</td>\n      <td>2445856</td>\n      <td>1.377283</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Florida</th>\n      <td>848226468</td>\n      <td>0</td>\n      <td>12478196</td>\n      <td>1.471093</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Hawaii</th>\n      <td>14144001</td>\n      <td>0</td>\n      <td>180921</td>\n      <td>1.279136</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Iowa</th>\n      <td>131506421</td>\n      <td>0</td>\n      <td>2033449</td>\n      <td>1.546274</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Idaho</th>\n      <td>68496689</td>\n      <td>0</td>\n      <td>746126</td>\n      <td>1.089288</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Kansas</th>\n      <td>110929450</td>\n      <td>0</td>\n      <td>1602300</td>\n      <td>1.444432</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Kentucky</th>\n      <td>159177697</td>\n      <td>0</td>\n      <td>2161617</td>\n      <td>1.357990</td>\n      <td>0.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>South Carolina</th>\n      <td>207059600</td>\n      <td>0</td>\n      <td>3424848</td>\n      <td>1.654040</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Texas</th>\n      <td>1038325285</td>\n      <td>0</td>\n      <td>17348402</td>\n      <td>1.670806</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Virginia</th>\n      <td>227526385</td>\n      <td>0</td>\n      <td>3698877</td>\n      <td>1.625691</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>West Virginia</th>\n      <td>52832389</td>\n      <td>0</td>\n      <td>903528</td>\n      <td>1.710178</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Alabama</th>\n      <td>200323493</td>\n      <td>0</td>\n      <td>3723145</td>\n      <td>1.858566</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Arkansas</th>\n      <td>124239415</td>\n      <td>0</td>\n      <td>2041981</td>\n      <td>1.643585</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Arizona</th>\n      <td>307229820</td>\n      <td>0</td>\n      <td>6015311</td>\n      <td>1.957919</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>California</th>\n      <td>1262016103</td>\n      <td>0</td>\n      <td>19931034</td>\n      <td>1.579301</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Delaware</th>\n      <td>35547897</td>\n      <td>0</td>\n      <td>615046</td>\n      <td>1.730190</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Georgia</th>\n      <td>319233753</td>\n      <td>0</td>\n      <td>6882707</td>\n      <td>2.156009</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Illinois</th>\n      <td>476018159</td>\n      <td>0</td>\n      <td>9074100</td>\n      <td>1.906251</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Indiana</th>\n      <td>257025658</td>\n      <td>0</td>\n      <td>4783841</td>\n      <td>1.861231</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Louisiana</th>\n      <td>185972187</td>\n      <td>0</td>\n      <td>4319891</td>\n      <td>2.322869</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Maryland</th>\n      <td>158010274</td>\n      <td>0</td>\n      <td>3491665</td>\n      <td>2.209771</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Mississippi</th>\n      <td>121279152</td>\n      <td>0</td>\n      <td>2749046</td>\n      <td>2.266709</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "cluster_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "KeyError",
     "evalue": "'Recovered'",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mD:\\software\\anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3360\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3361\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\software\\anaconda\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mD:\\software\\anaconda\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Recovered'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_4396/4068643737.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mseparate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcountrywise\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_4396/4068643737.py\u001b[0m in \u001b[0;36mseparate\u001b[1;34m(countrywise)\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcountrywise\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Recovery\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcountrywise\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Mortality\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcountrywise\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Clusters\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     plt.axvline(((datewise[\"Recovered\"]/datewise[\"Confirmed\"])*100).mean(),\n\u001b[0m\u001b[0;32m      5\u001b[0m                 color='red',linestyle=\"--\",label=\"Mean Recovery Rate around the World\")\n\u001b[0;32m      6\u001b[0m     plt.axhline(((datewise[\"Deaths\"]/datewise[\"Confirmed\"])*100).mean(),\n",
      "\u001b[1;32mD:\\software\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3456\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3457\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3458\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3459\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\software\\anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3361\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3363\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Recovered'"
     ]
    }
   ],
   "source": [
    "def separate(countrywise):\n",
    "    plt.figure(figsize=(10,5))\n",
    "    sns.scatterplot(x=countrywise[\"Recovery\"],y=countrywise[\"Mortality\"],hue=countrywise[\"Clusters\"],s=100)\n",
    "    plt.axvline(((datewise[\"Recovered\"]/datewise[\"Confirmed\"])*100).mean(),\n",
    "                color='red',linestyle=\"--\",label=\"Mean Recovery Rate around the World\")\n",
    "    plt.axhline(((datewise[\"Deaths\"]/datewise[\"Confirmed\"])*100).mean(),\n",
    "                color='black',linestyle=\"--\",label=\"Mean Mortality Rate around the World\")\n",
    "    plt.legend()\n",
    "\n",
    "separate(countrywise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x360 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"313.45pt\" version=\"1.1\" viewBox=\"0 0 606.525 313.45\" width=\"606.525pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-12-03T19:49:43.881916</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.4.3, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n  <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n  <g id=\"patch_1\">\r\n   <path d=\"M 0 313.45 \r\nL 606.525 313.45 \r\nL 606.525 0 \r\nL 0 0 \r\nz\r\n\" style=\"fill:none;\"/>\r\n  </g>\r\n  <g id=\"axes_1\">\r\n   <g id=\"patch_2\">\r\n    <path d=\"M 41.325 279 \r\nL 599.325 279 \r\nL 599.325 7.2 \r\nL 41.325 7.2 \r\nz\r\n\" style=\"fill:#ffffff;\"/>\r\n   </g>\r\n   <g id=\"PathCollection_1\">\r\n    <defs>\r\n     <path d=\"M 0 5 \r\nC 1.326016 5 2.597899 4.473168 3.535534 3.535534 \r\nC 4.473168 2.597899 5 1.326016 5 -0 \r\nC 5 -1.326016 4.473168 -2.597899 3.535534 -3.535534 \r\nC 2.597899 -4.473168 1.326016 -5 0 -5 \r\nC -1.326016 -5 -2.597899 -4.473168 -3.535534 -3.535534 \r\nC -4.473168 -2.597899 -5 -1.326016 -5 0 \r\nC -5 1.326016 -4.473168 2.597899 -3.535534 3.535534 \r\nC -2.597899 4.473168 -1.326016 5 0 5 \r\nz\r\n\" id=\"C0_0_98f447cc8a\"/>\r\n    </defs>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"165.328963\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"176.905197\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"189.791837\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"163.845728\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"263.063404\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"167.836788\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"203.618913\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"184.492581\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"218.263571\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"160.362677\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"212.153081\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"266.645455\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"147.235536\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"166.253794\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"138.446313\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"171.940708\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"189.812198\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#a9678f;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"41.21547\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#a9678f;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"80.859577\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"158.592375\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"181.513369\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"120.92234\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"198.494787\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"174.862486\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"215.289694\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"143.017134\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"146.999814\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"183.871926\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"191.518982\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"106.161013\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#a9678f;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"23.648324\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"116.166262\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"191.711489\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#a9678f;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"91.257016\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"195.270933\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"177.873345\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"111.129196\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"191.573231\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"196.256432\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"192.8514\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"223.26659\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"169.448986\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#a9678f;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"19.554545\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"128.463889\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"162.913215\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#a9678f;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"21.270702\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"155.026964\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"191.256524\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#2d1e3e;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"195.239607\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#a9678f;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"88.208162\"/>\r\n    </g>\r\n    <g clip-path=\"url(#p27a8f6a265)\">\r\n     <use style=\"fill:#edd1cb;stroke:#ffffff;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#C0_0_98f447cc8a\" y=\"118.605316\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"PathCollection_2\"/>\r\n   <g id=\"PathCollection_3\"/>\r\n   <g id=\"PathCollection_4\"/>\r\n   <g id=\"matplotlib.axis_1\">\r\n    <g id=\"xtick_1\">\r\n     <g id=\"line2d_1\">\r\n      <defs>\r\n       <path d=\"M 0 0 \r\nL 0 3.5 \r\n\" id=\"mb5161135dc\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n      </defs>\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"117.415909\" xlink:href=\"#mb5161135dc\" y=\"279\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_1\">\r\n      <!-- -0.04 -->\r\n      <g transform=\"translate(104.915909 292.875)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 2975 2125 \r\nL 125 2125 \r\nL 125 2525 \r\nL 2975 2525 \r\nL 2975 2125 \r\nz\r\n\" id=\"SimHei-2d\" transform=\"scale(0.015625)\"/>\r\n        <path d=\"M 225 2537 \r\nQ 250 3200 412 3587 \r\nQ 575 3975 875 4225 \r\nQ 1175 4475 1612 4475 \r\nQ 2050 4475 2375 4112 \r\nQ 2700 3750 2800 3200 \r\nQ 2900 2650 2862 1937 \r\nQ 2825 1225 2612 775 \r\nQ 2400 325 1975 150 \r\nQ 1550 -25 1125 187 \r\nQ 700 400 525 750 \r\nQ 350 1100 275 1487 \r\nQ 200 1875 225 2537 \r\nz\r\nM 750 2687 \r\nQ 675 2000 800 1462 \r\nQ 925 925 1212 700 \r\nQ 1500 475 1800 612 \r\nQ 2100 750 2237 1162 \r\nQ 2375 1575 2375 2062 \r\nQ 2375 2550 2337 2950 \r\nQ 2300 3350 2112 3675 \r\nQ 1925 4000 1612 4012 \r\nQ 1300 4025 1062 3700 \r\nQ 825 3375 750 2687 \r\nz\r\n\" id=\"SimHei-30\" transform=\"scale(0.015625)\"/>\r\n        <path d=\"M 1075 125 \r\nL 500 125 \r\nL 500 675 \r\nL 1075 675 \r\nL 1075 125 \r\nz\r\n\" id=\"SimHei-2e\" transform=\"scale(0.015625)\"/>\r\n        <path d=\"M 2000 1100 \r\nL 75 1100 \r\nL 75 1525 \r\nL 2100 4450 \r\nL 2475 4450 \r\nL 2475 1525 \r\nL 3075 1525 \r\nL 3075 1100 \r\nL 2475 1100 \r\nL 2475 150 \r\nL 2000 150 \r\nL 2000 1100 \r\nz\r\nM 2000 1525 \r\nL 2000 3500 \r\nL 600 1525 \r\nL 2000 1525 \r\nz\r\n\" id=\"SimHei-34\" transform=\"scale(0.015625)\"/>\r\n       </defs>\r\n       <use xlink:href=\"#SimHei-2d\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-30\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-2e\"/>\r\n       <use x=\"150\" xlink:href=\"#SimHei-30\"/>\r\n       <use x=\"200\" xlink:href=\"#SimHei-34\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_2\">\r\n     <g id=\"line2d_2\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"218.870455\" xlink:href=\"#mb5161135dc\" y=\"279\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_2\">\r\n      <!-- -0.02 -->\r\n      <g transform=\"translate(206.370455 292.875)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 300 250 \r\nQ 325 625 650 925 \r\nQ 975 1225 1475 1862 \r\nQ 1975 2500 2125 2850 \r\nQ 2275 3200 2237 3450 \r\nQ 2200 3700 2000 3862 \r\nQ 1800 4025 1537 4000 \r\nQ 1275 3975 1037 3800 \r\nQ 800 3625 675 3275 \r\nL 200 3350 \r\nQ 400 3925 712 4187 \r\nQ 1025 4450 1450 4475 \r\nQ 1700 4500 1900 4462 \r\nQ 2100 4425 2312 4287 \r\nQ 2525 4150 2662 3875 \r\nQ 2800 3600 2762 3212 \r\nQ 2725 2825 2375 2287 \r\nQ 2025 1750 1025 600 \r\nL 2825 600 \r\nL 2825 150 \r\nL 300 150 \r\nL 300 250 \r\nz\r\n\" id=\"SimHei-32\" transform=\"scale(0.015625)\"/>\r\n       </defs>\r\n       <use xlink:href=\"#SimHei-2d\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-30\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-2e\"/>\r\n       <use x=\"150\" xlink:href=\"#SimHei-30\"/>\r\n       <use x=\"200\" xlink:href=\"#SimHei-32\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_3\">\r\n     <g id=\"line2d_3\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"320.325\" xlink:href=\"#mb5161135dc\" y=\"279\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_3\">\r\n      <!-- 0.00 -->\r\n      <g transform=\"translate(310.325 292.875)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#SimHei-30\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-2e\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-30\"/>\r\n       <use x=\"150\" xlink:href=\"#SimHei-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_4\">\r\n     <g id=\"line2d_4\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"421.779545\" xlink:href=\"#mb5161135dc\" y=\"279\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_4\">\r\n      <!-- 0.02 -->\r\n      <g transform=\"translate(411.779545 292.875)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#SimHei-30\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-2e\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-30\"/>\r\n       <use x=\"150\" xlink:href=\"#SimHei-32\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_5\">\r\n     <g id=\"line2d_5\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"523.234091\" xlink:href=\"#mb5161135dc\" y=\"279\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_5\">\r\n      <!-- 0.04 -->\r\n      <g transform=\"translate(513.234091 292.875)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#SimHei-30\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-2e\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-30\"/>\r\n       <use x=\"150\" xlink:href=\"#SimHei-34\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"text_6\">\r\n     <!-- Recovery -->\r\n     <g transform=\"translate(300.325 305)scale(0.1 -0.1)\">\r\n      <defs>\r\n       <path d=\"M 3000 125 \r\nL 2400 125 \r\nL 1500 1975 \r\nL 850 1975 \r\nL 850 125 \r\nL 275 125 \r\nL 275 4400 \r\nL 1525 4400 \r\nQ 2125 4400 2500 4112 \r\nQ 2875 3825 2875 3175 \r\nQ 2875 2675 2625 2400 \r\nQ 2375 2125 2050 2050 \r\nL 3000 125 \r\nz\r\nM 2300 3175 \r\nQ 2300 3525 2087 3725 \r\nQ 1875 3925 1400 3925 \r\nL 850 3925 \r\nL 850 2450 \r\nL 1550 2450 \r\nQ 1850 2450 2075 2612 \r\nQ 2300 2775 2300 3175 \r\nz\r\n\" id=\"SimHei-52\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 2850 1075 \r\nQ 2800 625 2450 350 \r\nQ 2100 75 1625 75 \r\nQ 1025 75 637 462 \r\nQ 250 850 250 1525 \r\nQ 250 2200 637 2587 \r\nQ 1025 2975 1625 2975 \r\nQ 2150 2975 2487 2637 \r\nQ 2825 2300 2825 1525 \r\nL 800 1525 \r\nQ 800 975 1037 750 \r\nQ 1275 525 1625 525 \r\nQ 1900 525 2075 662 \r\nQ 2250 800 2300 1075 \r\nL 2850 1075 \r\nz\r\nM 2250 1925 \r\nQ 2200 2275 2025 2412 \r\nQ 1850 2550 1575 2550 \r\nQ 1325 2550 1125 2412 \r\nQ 925 2275 825 1925 \r\nL 2250 1925 \r\nz\r\n\" id=\"SimHei-65\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 2850 1250 \r\nQ 2850 725 2487 400 \r\nQ 2125 75 1575 75 \r\nQ 1025 75 625 462 \r\nQ 225 850 225 1525 \r\nQ 225 2200 625 2587 \r\nQ 1025 2975 1575 2975 \r\nQ 2125 2975 2450 2687 \r\nQ 2775 2400 2775 2000 \r\nL 2225 2000 \r\nQ 2200 2300 2012 2412 \r\nQ 1825 2525 1575 2525 \r\nQ 1275 2525 1025 2287 \r\nQ 775 2050 775 1525 \r\nQ 775 1000 1025 762 \r\nQ 1275 525 1575 525 \r\nQ 1900 525 2100 700 \r\nQ 2300 875 2300 1250 \r\nL 2850 1250 \r\nz\r\n\" id=\"SimHei-63\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 2925 1525 \r\nQ 2925 875 2525 475 \r\nQ 2125 75 1575 75 \r\nQ 1025 75 625 475 \r\nQ 225 875 225 1525 \r\nQ 225 2175 625 2575 \r\nQ 1025 2975 1575 2975 \r\nQ 2125 2975 2525 2575 \r\nQ 2925 2175 2925 1525 \r\nz\r\nM 2375 1525 \r\nQ 2375 2025 2125 2275 \r\nQ 1875 2525 1575 2525 \r\nQ 1275 2525 1025 2275 \r\nQ 775 2025 775 1525 \r\nQ 775 1025 1025 775 \r\nQ 1275 525 1575 525 \r\nQ 1875 525 2125 775 \r\nQ 2375 1025 2375 1525 \r\nz\r\n\" id=\"SimHei-6f\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 2875 2925 \r\nL 1825 75 \r\nL 1275 75 \r\nL 225 2925 \r\nL 750 2925 \r\nL 1525 750 \r\nL 1575 750 \r\nL 2350 2925 \r\nL 2875 2925 \r\nz\r\n\" id=\"SimHei-76\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 2500 2425 \r\nQ 2025 2500 1700 2287 \r\nQ 1375 2075 1150 1550 \r\nL 1150 125 \r\nL 650 125 \r\nL 650 2925 \r\nL 1150 2925 \r\nL 1150 2200 \r\nQ 1375 2600 1712 2787 \r\nQ 2050 2975 2500 2975 \r\nL 2500 2425 \r\nz\r\n\" id=\"SimHei-72\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 2900 2925 \r\nL 1750 -100 \r\nQ 1650 -450 1450 -625 \r\nQ 1250 -800 950 -800 \r\nQ 825 -800 712 -787 \r\nQ 600 -775 500 -725 \r\nL 500 -225 \r\nQ 600 -275 700 -300 \r\nQ 800 -325 875 -325 \r\nQ 1075 -325 1150 -262 \r\nQ 1225 -200 1300 25 \r\nL 200 2925 \r\nL 750 2925 \r\nL 1550 725 \r\nL 1575 725 \r\nL 2350 2925 \r\nL 2900 2925 \r\nz\r\n\" id=\"SimHei-79\" transform=\"scale(0.015625)\"/>\r\n      </defs>\r\n      <use xlink:href=\"#SimHei-52\"/>\r\n      <use x=\"50\" xlink:href=\"#SimHei-65\"/>\r\n      <use x=\"100\" xlink:href=\"#SimHei-63\"/>\r\n      <use x=\"150\" xlink:href=\"#SimHei-6f\"/>\r\n      <use x=\"200\" xlink:href=\"#SimHei-76\"/>\r\n      <use x=\"250\" xlink:href=\"#SimHei-65\"/>\r\n      <use x=\"300\" xlink:href=\"#SimHei-72\"/>\r\n      <use x=\"350\" xlink:href=\"#SimHei-79\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"matplotlib.axis_2\">\r\n    <g id=\"ytick_1\">\r\n     <g id=\"line2d_6\">\r\n      <defs>\r\n       <path d=\"M 0 0 \r\nL -3.5 0 \r\n\" id=\"m7e201cc419\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n      </defs>\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"41.325\" xlink:href=\"#m7e201cc419\" y=\"267.42097\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_7\">\r\n      <!-- 0.5 -->\r\n      <g transform=\"translate(19.325 270.85847)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 550 1325 \r\nQ 725 650 1150 575 \r\nQ 1575 500 1837 662 \r\nQ 2100 825 2212 1087 \r\nQ 2325 1350 2312 1675 \r\nQ 2300 2000 2137 2225 \r\nQ 1975 2450 1725 2525 \r\nQ 1475 2600 1162 2525 \r\nQ 850 2450 650 2175 \r\nL 225 2225 \r\nQ 275 2375 700 4375 \r\nL 2675 4375 \r\nL 2675 3925 \r\nL 1075 3925 \r\nQ 950 3250 825 2850 \r\nQ 1200 3025 1525 3012 \r\nQ 1850 3000 2150 2862 \r\nQ 2450 2725 2587 2487 \r\nQ 2725 2250 2787 2012 \r\nQ 2850 1775 2837 1500 \r\nQ 2825 1225 2725 937 \r\nQ 2625 650 2425 462 \r\nQ 2225 275 1937 162 \r\nQ 1650 50 1275 75 \r\nQ 900 100 562 350 \r\nQ 225 600 100 1200 \r\nL 550 1325 \r\nz\r\n\" id=\"SimHei-35\" transform=\"scale(0.015625)\"/>\r\n       </defs>\r\n       <use xlink:href=\"#SimHei-30\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-2e\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-35\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_2\">\r\n     <g id=\"line2d_7\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"41.325\" xlink:href=\"#m7e201cc419\" y=\"223.18852\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_8\">\r\n      <!-- 1.0 -->\r\n      <g transform=\"translate(19.325 226.62602)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 1400 3600 \r\nQ 1075 3275 575 2975 \r\nL 575 3450 \r\nQ 1200 3875 1600 4450 \r\nL 1900 4450 \r\nL 1900 150 \r\nL 1400 150 \r\nL 1400 3600 \r\nz\r\n\" id=\"SimHei-31\" transform=\"scale(0.015625)\"/>\r\n       </defs>\r\n       <use xlink:href=\"#SimHei-31\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-2e\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_3\">\r\n     <g id=\"line2d_8\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"41.325\" xlink:href=\"#m7e201cc419\" y=\"178.95607\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_9\">\r\n      <!-- 1.5 -->\r\n      <g transform=\"translate(19.325 182.39357)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#SimHei-31\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-2e\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-35\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_4\">\r\n     <g id=\"line2d_9\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"41.325\" xlink:href=\"#m7e201cc419\" y=\"134.723621\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_10\">\r\n      <!-- 2.0 -->\r\n      <g transform=\"translate(19.325 138.161121)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#SimHei-32\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-2e\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_5\">\r\n     <g id=\"line2d_10\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"41.325\" xlink:href=\"#m7e201cc419\" y=\"90.491171\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_11\">\r\n      <!-- 2.5 -->\r\n      <g transform=\"translate(19.325 93.928671)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#SimHei-32\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-2e\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-35\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_6\">\r\n     <g id=\"line2d_11\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"41.325\" xlink:href=\"#m7e201cc419\" y=\"46.258721\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_12\">\r\n      <!-- 3.0 -->\r\n      <g transform=\"translate(19.325 49.696221)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 250 1225 \r\nL 700 1300 \r\nQ 800 975 1025 762 \r\nQ 1250 550 1587 562 \r\nQ 1925 575 2125 837 \r\nQ 2325 1100 2300 1437 \r\nQ 2275 1775 2037 1962 \r\nQ 1800 2150 1275 2225 \r\nL 1275 2550 \r\nQ 1800 2600 2037 2825 \r\nQ 2275 3050 2250 3412 \r\nQ 2225 3775 1925 3937 \r\nQ 1625 4100 1287 3975 \r\nQ 950 3850 750 3275 \r\nL 300 3350 \r\nQ 450 3800 712 4100 \r\nQ 975 4400 1425 4450 \r\nQ 1875 4500 2212 4337 \r\nQ 2550 4175 2687 3837 \r\nQ 2825 3500 2725 3100 \r\nQ 2625 2700 2150 2400 \r\nQ 2500 2250 2687 1950 \r\nQ 2875 1650 2812 1162 \r\nQ 2750 675 2375 375 \r\nQ 2000 75 1525 87 \r\nQ 1050 100 700 387 \r\nQ 350 675 250 1225 \r\nz\r\n\" id=\"SimHei-33\" transform=\"scale(0.015625)\"/>\r\n       </defs>\r\n       <use xlink:href=\"#SimHei-33\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-2e\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"text_13\">\r\n     <!-- Mortality -->\r\n     <g transform=\"translate(14.075 165.6)rotate(-90)scale(0.1 -0.1)\">\r\n      <defs>\r\n       <path d=\"M 2925 125 \r\nL 2425 125 \r\nL 2425 3175 \r\nL 2375 3175 \r\nL 1775 125 \r\nL 1375 125 \r\nL 775 3175 \r\nL 725 3175 \r\nL 725 125 \r\nL 225 125 \r\nL 225 4400 \r\nL 1000 4400 \r\nL 1550 1575 \r\nL 1600 1575 \r\nL 2150 4400 \r\nL 2925 4400 \r\nL 2925 125 \r\nz\r\n\" id=\"SimHei-4d\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 2750 200 \r\nQ 2625 150 2462 112 \r\nQ 2300 75 2025 75 \r\nQ 1575 75 1300 325 \r\nQ 1025 575 1025 1025 \r\nL 1025 2525 \r\nL 175 2525 \r\nL 175 2925 \r\nL 1025 2925 \r\nL 1025 3900 \r\nL 1525 3900 \r\nL 1525 2925 \r\nL 2550 2925 \r\nL 2550 2525 \r\nL 1525 2525 \r\nL 1525 1000 \r\nQ 1525 800 1625 662 \r\nQ 1725 525 2000 525 \r\nQ 2275 525 2450 575 \r\nQ 2625 625 2750 700 \r\nL 2750 200 \r\nz\r\n\" id=\"SimHei-74\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 2875 125 \r\nL 2275 125 \r\nQ 2225 175 2200 262 \r\nQ 2175 350 2175 475 \r\nQ 2000 275 1750 175 \r\nQ 1500 75 1225 75 \r\nQ 825 75 550 275 \r\nQ 275 475 275 850 \r\nQ 275 1225 525 1450 \r\nQ 775 1675 1300 1750 \r\nQ 1650 1800 1912 1875 \r\nQ 2175 1950 2175 2075 \r\nQ 2175 2225 2062 2375 \r\nQ 1950 2525 1575 2525 \r\nQ 1275 2525 1137 2412 \r\nQ 1000 2300 950 2100 \r\nL 400 2100 \r\nQ 450 2500 762 2737 \r\nQ 1075 2975 1575 2975 \r\nQ 2125 2975 2400 2725 \r\nQ 2675 2475 2675 2025 \r\nL 2675 650 \r\nQ 2675 500 2725 375 \r\nQ 2775 250 2875 125 \r\nz\r\nM 2175 1050 \r\nL 2175 1550 \r\nQ 2025 1500 1887 1462 \r\nQ 1750 1425 1425 1375 \r\nQ 1050 1325 937 1200 \r\nQ 825 1075 825 900 \r\nQ 825 750 937 637 \r\nQ 1050 525 1275 525 \r\nQ 1500 525 1762 650 \r\nQ 2025 775 2175 1050 \r\nz\r\n\" id=\"SimHei-61\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 1825 125 \r\nL 1325 125 \r\nL 1325 4400 \r\nL 1825 4400 \r\nL 1825 125 \r\nz\r\n\" id=\"SimHei-6c\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 1800 3725 \r\nL 1300 3725 \r\nL 1300 4375 \r\nL 1800 4375 \r\nL 1800 3725 \r\nz\r\nM 1800 125 \r\nL 1300 125 \r\nL 1300 2925 \r\nL 1800 2925 \r\nL 1800 125 \r\nz\r\n\" id=\"SimHei-69\" transform=\"scale(0.015625)\"/>\r\n      </defs>\r\n      <use xlink:href=\"#SimHei-4d\"/>\r\n      <use x=\"50\" xlink:href=\"#SimHei-6f\"/>\r\n      <use x=\"100\" xlink:href=\"#SimHei-72\"/>\r\n      <use x=\"150\" xlink:href=\"#SimHei-74\"/>\r\n      <use x=\"200\" xlink:href=\"#SimHei-61\"/>\r\n      <use x=\"250\" xlink:href=\"#SimHei-6c\"/>\r\n      <use x=\"300\" xlink:href=\"#SimHei-69\"/>\r\n      <use x=\"350\" xlink:href=\"#SimHei-74\"/>\r\n      <use x=\"400\" xlink:href=\"#SimHei-79\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"line2d_12\">\r\n    <path clip-path=\"url(#p27a8f6a265)\" d=\"M 320.325 279 \r\nL 320.325 7.2 \r\n\" style=\"fill:none;stroke:#ff0000;stroke-dasharray:5.55,2.4;stroke-dashoffset:0;stroke-width:1.5;\"/>\r\n   </g>\r\n   <g id=\"line2d_13\">\r\n    <path clip-path=\"url(#p27a8f6a265)\" d=\"M 41.325 158.633487 \r\nL 599.325 158.633487 \r\n\" style=\"fill:none;stroke:#000000;stroke-dasharray:5.55,2.4;stroke-dashoffset:0;stroke-width:1.5;\"/>\r\n   </g>\r\n   <g id=\"patch_3\">\r\n    <path d=\"M 41.325 279 \r\nL 41.325 7.2 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_4\">\r\n    <path d=\"M 599.325 279 \r\nL 599.325 7.2 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_5\">\r\n    <path d=\"M 41.325 279 \r\nL 599.325 279 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_6\">\r\n    <path d=\"M 41.325 7.2 \r\nL 599.325 7.2 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"legend_1\">\r\n    <g id=\"patch_7\">\r\n     <path d=\"M 380.325 81.45 \r\nL 592.325 81.45 \r\nQ 594.325 81.45 594.325 79.45 \r\nL 594.325 14.2 \r\nQ 594.325 12.2 592.325 12.2 \r\nL 380.325 12.2 \r\nQ 378.325 12.2 378.325 14.2 \r\nL 378.325 79.45 \r\nQ 378.325 81.45 380.325 81.45 \r\nz\r\n\" style=\"fill:#ffffff;opacity:0.8;stroke:#cccccc;stroke-linejoin:miter;\"/>\r\n    </g>\r\n    <g id=\"line2d_14\">\r\n     <path d=\"M 382.325 19.7 \r\nL 402.325 19.7 \r\n\" style=\"fill:none;stroke:#ff0000;stroke-dasharray:5.55,2.4;stroke-dashoffset:0;stroke-width:1.5;\"/>\r\n    </g>\r\n    <g id=\"line2d_15\"/>\r\n    <g id=\"text_14\">\r\n     <!-- Mean Recovery Rate around the World -->\r\n     <g transform=\"translate(410.325 23.2)scale(0.1 -0.1)\">\r\n      <defs>\r\n       <path d=\"M 2800 125 \r\nL 2300 125 \r\nL 2300 1925 \r\nQ 2300 2225 2150 2400 \r\nQ 2000 2575 1750 2575 \r\nQ 1425 2575 1137 2237 \r\nQ 850 1900 850 1400 \r\nL 850 125 \r\nL 350 125 \r\nL 350 2925 \r\nL 850 2925 \r\nL 850 2400 \r\nQ 1050 2675 1287 2825 \r\nQ 1525 2975 1900 2975 \r\nQ 2350 2975 2575 2725 \r\nQ 2800 2475 2800 2100 \r\nL 2800 125 \r\nz\r\n\" id=\"SimHei-6e\" transform=\"scale(0.015625)\"/>\r\n       <path id=\"SimHei-20\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 2800 125 \r\nL 2300 125 \r\nL 2300 650 \r\nQ 2100 375 1862 225 \r\nQ 1625 75 1250 75 \r\nQ 800 75 575 325 \r\nQ 350 575 350 950 \r\nL 350 2925 \r\nL 850 2925 \r\nL 850 1125 \r\nQ 850 825 1000 650 \r\nQ 1150 475 1400 475 \r\nQ 1725 475 2012 812 \r\nQ 2300 1150 2300 1650 \r\nL 2300 2925 \r\nL 2800 2925 \r\nL 2800 125 \r\nz\r\n\" id=\"SimHei-75\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 2750 125 \r\nL 2250 125 \r\nL 2250 500 \r\nQ 2100 275 1900 175 \r\nQ 1700 75 1425 75 \r\nQ 925 75 575 437 \r\nQ 225 800 225 1525 \r\nQ 225 2250 575 2625 \r\nQ 925 3000 1425 3000 \r\nQ 1700 3000 1900 2887 \r\nQ 2100 2775 2250 2550 \r\nL 2250 4400 \r\nL 2750 4400 \r\nL 2750 125 \r\nz\r\nM 2250 1525 \r\nQ 2250 2000 2037 2275 \r\nQ 1825 2550 1525 2550 \r\nQ 1150 2550 962 2275 \r\nQ 775 2000 775 1525 \r\nQ 775 1050 962 787 \r\nQ 1150 525 1525 525 \r\nQ 1825 525 2037 787 \r\nQ 2250 1050 2250 1525 \r\nz\r\n\" id=\"SimHei-64\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 2800 125 \r\nL 2300 125 \r\nL 2300 1925 \r\nQ 2300 2225 2150 2400 \r\nQ 2000 2575 1750 2575 \r\nQ 1425 2575 1137 2237 \r\nQ 850 1900 850 1400 \r\nL 850 125 \r\nL 350 125 \r\nL 350 4400 \r\nL 850 4400 \r\nL 850 2400 \r\nQ 1050 2675 1287 2825 \r\nQ 1525 2975 1900 2975 \r\nQ 2350 2975 2575 2725 \r\nQ 2800 2475 2800 2100 \r\nL 2800 125 \r\nz\r\n\" id=\"SimHei-68\" transform=\"scale(0.015625)\"/>\r\n       <path d=\"M 3075 4400 \r\nL 2525 75 \r\nL 2025 75 \r\nL 1575 3275 \r\nL 1525 3275 \r\nL 1075 75 \r\nL 575 75 \r\nL 25 4400 \r\nL 575 4400 \r\nL 850 1675 \r\nL 900 1675 \r\nL 1275 4400 \r\nL 1825 4400 \r\nL 2200 1675 \r\nL 2250 1675 \r\nL 2525 4400 \r\nL 3075 4400 \r\nz\r\n\" id=\"SimHei-57\" transform=\"scale(0.015625)\"/>\r\n      </defs>\r\n      <use xlink:href=\"#SimHei-4d\"/>\r\n      <use x=\"50\" xlink:href=\"#SimHei-65\"/>\r\n      <use x=\"100\" xlink:href=\"#SimHei-61\"/>\r\n      <use x=\"150\" xlink:href=\"#SimHei-6e\"/>\r\n      <use x=\"200\" xlink:href=\"#SimHei-20\"/>\r\n      <use x=\"250\" xlink:href=\"#SimHei-52\"/>\r\n      <use x=\"300\" xlink:href=\"#SimHei-65\"/>\r\n      <use x=\"350\" xlink:href=\"#SimHei-63\"/>\r\n      <use x=\"400\" xlink:href=\"#SimHei-6f\"/>\r\n      <use x=\"450\" xlink:href=\"#SimHei-76\"/>\r\n      <use x=\"500\" xlink:href=\"#SimHei-65\"/>\r\n      <use x=\"550\" xlink:href=\"#SimHei-72\"/>\r\n      <use x=\"600\" xlink:href=\"#SimHei-79\"/>\r\n      <use x=\"650\" xlink:href=\"#SimHei-20\"/>\r\n      <use x=\"700\" xlink:href=\"#SimHei-52\"/>\r\n      <use x=\"750\" xlink:href=\"#SimHei-61\"/>\r\n      <use x=\"800\" xlink:href=\"#SimHei-74\"/>\r\n      <use x=\"850\" xlink:href=\"#SimHei-65\"/>\r\n      <use x=\"900\" xlink:href=\"#SimHei-20\"/>\r\n      <use x=\"950\" xlink:href=\"#SimHei-61\"/>\r\n      <use x=\"1000\" xlink:href=\"#SimHei-72\"/>\r\n      <use x=\"1050\" xlink:href=\"#SimHei-6f\"/>\r\n      <use x=\"1100\" xlink:href=\"#SimHei-75\"/>\r\n      <use x=\"1150\" xlink:href=\"#SimHei-6e\"/>\r\n      <use x=\"1200\" xlink:href=\"#SimHei-64\"/>\r\n      <use x=\"1250\" xlink:href=\"#SimHei-20\"/>\r\n      <use x=\"1300\" xlink:href=\"#SimHei-74\"/>\r\n      <use x=\"1350\" xlink:href=\"#SimHei-68\"/>\r\n      <use x=\"1400\" xlink:href=\"#SimHei-65\"/>\r\n      <use x=\"1450\" xlink:href=\"#SimHei-20\"/>\r\n      <use x=\"1500\" xlink:href=\"#SimHei-57\"/>\r\n      <use x=\"1550\" xlink:href=\"#SimHei-6f\"/>\r\n      <use x=\"1600\" xlink:href=\"#SimHei-72\"/>\r\n      <use x=\"1650\" xlink:href=\"#SimHei-6c\"/>\r\n      <use x=\"1700\" xlink:href=\"#SimHei-64\"/>\r\n     </g>\r\n    </g>\r\n    <g id=\"line2d_16\">\r\n     <path d=\"M 382.325 32.95 \r\nL 402.325 32.95 \r\n\" style=\"fill:none;stroke:#000000;stroke-dasharray:5.55,2.4;stroke-dashoffset:0;stroke-width:1.5;\"/>\r\n    </g>\r\n    <g id=\"line2d_17\"/>\r\n    <g id=\"text_15\">\r\n     <!-- Mean Mortality Rate around the World -->\r\n     <g transform=\"translate(410.325 36.45)scale(0.1 -0.1)\">\r\n      <use xlink:href=\"#SimHei-4d\"/>\r\n      <use x=\"50\" xlink:href=\"#SimHei-65\"/>\r\n      <use x=\"100\" xlink:href=\"#SimHei-61\"/>\r\n      <use x=\"150\" xlink:href=\"#SimHei-6e\"/>\r\n      <use x=\"200\" xlink:href=\"#SimHei-20\"/>\r\n      <use x=\"250\" xlink:href=\"#SimHei-4d\"/>\r\n      <use x=\"300\" xlink:href=\"#SimHei-6f\"/>\r\n      <use x=\"350\" xlink:href=\"#SimHei-72\"/>\r\n      <use x=\"400\" xlink:href=\"#SimHei-74\"/>\r\n      <use x=\"450\" xlink:href=\"#SimHei-61\"/>\r\n      <use x=\"500\" xlink:href=\"#SimHei-6c\"/>\r\n      <use x=\"550\" xlink:href=\"#SimHei-69\"/>\r\n      <use x=\"600\" xlink:href=\"#SimHei-74\"/>\r\n      <use x=\"650\" xlink:href=\"#SimHei-79\"/>\r\n      <use x=\"700\" xlink:href=\"#SimHei-20\"/>\r\n      <use x=\"750\" xlink:href=\"#SimHei-52\"/>\r\n      <use x=\"800\" xlink:href=\"#SimHei-61\"/>\r\n      <use x=\"850\" xlink:href=\"#SimHei-74\"/>\r\n      <use x=\"900\" xlink:href=\"#SimHei-65\"/>\r\n      <use x=\"950\" xlink:href=\"#SimHei-20\"/>\r\n      <use x=\"1000\" xlink:href=\"#SimHei-61\"/>\r\n      <use x=\"1050\" xlink:href=\"#SimHei-72\"/>\r\n      <use x=\"1100\" xlink:href=\"#SimHei-6f\"/>\r\n      <use x=\"1150\" xlink:href=\"#SimHei-75\"/>\r\n      <use x=\"1200\" xlink:href=\"#SimHei-6e\"/>\r\n      <use x=\"1250\" xlink:href=\"#SimHei-64\"/>\r\n      <use x=\"1300\" xlink:href=\"#SimHei-20\"/>\r\n      <use x=\"1350\" xlink:href=\"#SimHei-74\"/>\r\n      <use x=\"1400\" xlink:href=\"#SimHei-68\"/>\r\n      <use x=\"1450\" xlink:href=\"#SimHei-65\"/>\r\n      <use x=\"1500\" xlink:href=\"#SimHei-20\"/>\r\n      <use x=\"1550\" xlink:href=\"#SimHei-57\"/>\r\n      <use x=\"1600\" xlink:href=\"#SimHei-6f\"/>\r\n      <use x=\"1650\" xlink:href=\"#SimHei-72\"/>\r\n      <use x=\"1700\" xlink:href=\"#SimHei-6c\"/>\r\n      <use x=\"1750\" xlink:href=\"#SimHei-64\"/>\r\n     </g>\r\n    </g>\r\n    <g id=\"PathCollection_5\">\r\n     <defs>\r\n      <path d=\"M 0 3 \r\nC 0.795609 3 1.55874 2.683901 2.12132 2.12132 \r\nC 2.683901 1.55874 3 0.795609 3 0 \r\nC 3 -0.795609 2.683901 -1.55874 2.12132 -2.12132 \r\nC 1.55874 -2.683901 0.795609 -3 0 -3 \r\nC -0.795609 -3 -1.55874 -2.683901 -2.12132 -2.12132 \r\nC -2.683901 -1.55874 -3 -0.795609 -3 0 \r\nC -3 0.795609 -2.683901 1.55874 -2.12132 2.12132 \r\nC -1.55874 2.683901 -0.795609 3 0 3 \r\nz\r\n\" id=\"mefbca6d1b2\" style=\"stroke:#edd1cb;\"/>\r\n     </defs>\r\n     <g>\r\n      <use style=\"fill:#edd1cb;stroke:#edd1cb;\" x=\"392.325\" xlink:href=\"#mefbca6d1b2\" y=\"47.075\"/>\r\n     </g>\r\n    </g>\r\n    <g id=\"text_16\">\r\n     <!-- 0 -->\r\n     <g transform=\"translate(410.325 49.7)scale(0.1 -0.1)\">\r\n      <use xlink:href=\"#SimHei-30\"/>\r\n     </g>\r\n    </g>\r\n    <g id=\"PathCollection_6\">\r\n     <defs>\r\n      <path d=\"M 0 3 \r\nC 0.795609 3 1.55874 2.683901 2.12132 2.12132 \r\nC 2.683901 1.55874 3 0.795609 3 0 \r\nC 3 -0.795609 2.683901 -1.55874 2.12132 -2.12132 \r\nC 1.55874 -2.683901 0.795609 -3 0 -3 \r\nC -0.795609 -3 -1.55874 -2.683901 -2.12132 -2.12132 \r\nC -2.683901 -1.55874 -3 -0.795609 -3 0 \r\nC -3 0.795609 -2.683901 1.55874 -2.12132 2.12132 \r\nC -1.55874 2.683901 -0.795609 3 0 3 \r\nz\r\n\" id=\"m59d999bc03\" style=\"stroke:#a9678f;\"/>\r\n     </defs>\r\n     <g>\r\n      <use style=\"fill:#a9678f;stroke:#a9678f;\" x=\"392.325\" xlink:href=\"#m59d999bc03\" y=\"60.325\"/>\r\n     </g>\r\n    </g>\r\n    <g id=\"text_17\">\r\n     <!-- 1 -->\r\n     <g transform=\"translate(410.325 62.95)scale(0.1 -0.1)\">\r\n      <use xlink:href=\"#SimHei-31\"/>\r\n     </g>\r\n    </g>\r\n    <g id=\"PathCollection_7\">\r\n     <defs>\r\n      <path d=\"M 0 3 \r\nC 0.795609 3 1.55874 2.683901 2.12132 2.12132 \r\nC 2.683901 1.55874 3 0.795609 3 0 \r\nC 3 -0.795609 2.683901 -1.55874 2.12132 -2.12132 \r\nC 1.55874 -2.683901 0.795609 -3 0 -3 \r\nC -0.795609 -3 -1.55874 -2.683901 -2.12132 -2.12132 \r\nC -2.683901 -1.55874 -3 -0.795609 -3 0 \r\nC -3 0.795609 -2.683901 1.55874 -2.12132 2.12132 \r\nC -1.55874 2.683901 -0.795609 3 0 3 \r\nz\r\n\" id=\"md6367223c7\" style=\"stroke:#2d1e3e;\"/>\r\n     </defs>\r\n     <g>\r\n      <use style=\"fill:#2d1e3e;stroke:#2d1e3e;\" x=\"392.325\" xlink:href=\"#md6367223c7\" y=\"73.575\"/>\r\n     </g>\r\n    </g>\r\n    <g id=\"text_18\">\r\n     <!-- 2 -->\r\n     <g transform=\"translate(410.325 76.2)scale(0.1 -0.1)\">\r\n      <use xlink:href=\"#SimHei-32\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n  </g>\r\n </g>\r\n <defs>\r\n  <clipPath id=\"p27a8f6a265\">\r\n   <rect height=\"271.8\" width=\"558\" x=\"41.325\" y=\"7.2\"/>\r\n  </clipPath>\r\n </defs>\r\n</svg>\r\n",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "def separate(countrywise):\n",
    "    plt.figure(figsize=(10,5))\n",
    "    sns.scatterplot(x=countrywise[\"Recovery\"],y=countrywise[\"Mortality\"],hue=countrywise[\"Clusters\"],s=100)\n",
    "    plt.axvline(countrywise[\"Recovery\"].mean(),color='red',linestyle=\"--\",label=\"Mean Recovery Rate around the World\")\n",
    "    plt.axhline(countrywise[\"Mortality\"].mean(),\n",
    "                color='black',linestyle=\"--\",label=\"Mean Mortality Rate around the World\")\n",
    "    plt.legend()\n",
    "\n",
    "separate(countrywise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.7 64-bit ('software': virtualenv)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "3.9.7"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  },
  "interpreter": {
   "hash": "c72f12f90170356fe3abc9748a888f8a0f253bf3dee690070f84c0db82f6a8c0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}